The present invention generally relates to a system and method for reducing noise in fluoroscopic image processing. In particular, the present invention relates to a system and method for spatiotemporal filtration of fluoroscopic images.
Imaging systems encompass a variety of imaging modalities, such as x-ray systems, computerized tomography (CT) systems, ultrasound systems, electron beam tomography (EBT) systems, magnetic resonance (MR) systems, and the like. Imaging systems generate images of an object, such as a patient, for example, through exposure to an energy source, such as x-rays passing through a patient, for example. The generated images may be used for many purposes. For instance, the images may be used for detecting internal defects in a structure, detecting fluid flow within a structure, or showing the presence or absence of objects in a structure. Additionally, imaging systems may be used for minimally-invasive medical procedures and used during image-guided surgery.
One particular type of medical imaging modality is digital fluoroscopic imaging. During fluoroscopic image acquisition, multiple x-ray images are taken successively to form an image sequence or video. Each individual x-ray image is called a frame. Each frame is made up of pixels. The number of x-ray photons reaching the x-ray detector is finite, which results in quantum image noise. Image noise is an undesired effect that may limit a visibility of anatomical features or become otherwise distracting or disturbing to a viewer.
Modern image processing techniques in fluoroscopic systems use various filters to reduce image noise and enhance the visibility of features of interest. An example of a type of filter used to reduce image noise is an adaptive filter. Generally, adaptive filters interpret values associated with pixels. If a pixel falls within a given range of values, the pixel is considered noise, and the pixel is recomputed. If a pixel is outside of a given range, then the pixel is presumed sufficiently accurate and allowed to pass through the filter. Traditionally, there are two techniques used to recalculate pixel value and reduce unwanted fluoroscopic image noise using adaptive filters: temporal and spatial filtration.
Temporal filtration compares a current value of a target pixel with previous values of the same target pixel. A temporal filter may recalculate a noisy pixel by comparing the current value of the target pixel with previous values of the target pixel. That is, a temporal filter may replace a noisy pixel value with an average value of that pixel from several previous frames.
Temporal noise filtration is most effective in static image sequences. During sequences of little motion, successive images contain similar information, producing an average closely resembling the true value of the pixel. However, if an object is moving, a pixel's value may widely vary over successive images. Hence, successive images may contain dissimilar information. Averaging of dissimilar information may produce a value that may not resemble the true value of the current pixel. Therefore, temporal filtration is an unsatisfactory method of enhancing a moving image because the averaging of frames may produce unwanted motion blur or motion lag.
Spatial filtration compares a target pixel's current value with values of the target pixel's neighbors. The neighboring pixels are then used to compute a new value for the noisy pixel. The neighbors of a pixel are the pixels surrounding the target pixel in a current frame. A typical neighborhood may be a four pixel neighborhood, which consists of the target pixel, and the pixels directly north, south, east, and west. A four pixel neighborhood forms a diamond shape around the target pixel. Another typical pixel neighborhood is an eight pixel neighborhood. An eight pixel neighborhood consists of the target pixel, and the pixels north, south, east, west, northeast, northwest, southeast, and southwest. An eight pixel neighborhood forms a box around the target pixel. Many configurations of pixel neighborhoods currently exist.
Unlike temporal filtration, spatial noise filtration is equally effective for filtering static and dynamic objects in image sequences. During image sequences containing motion, the value of a target pixel may vary widely. As explained above, an averaging of a target pixel's value with the target pixel's value in previous frames, would produce motion lag. Nevertheless, a target pixel's neighborhood in the current image may generally contain similar information to the true value of the target pixel. An average value amongst a neighborhood may produce a value closely resembling the true value of the target pixel.
However, averaging among a neighborhood in a current frame may introduce unwanted spatial artifacts. Some typical unwanted spatial artifacts may be lost edges, false edges, intra-region smoothing, segmented appearance, “patchiness,” or “blockiness.” The spatial artifacts, present in dynamic regions of an image, are generally more tolerable than the motion lag caused by temporal filtration with equivalent noise reduction. However, for static objects in an image, spatial filtration introduces artifacts while temporal filtration does not. The artifacts consistently degrade an image, making details of the image difficult to view. Therefore, spatial filtration is an unsatisfactory method of enhancing static objects in a sequence.
Various combinations of spatial and temporal filters currently exist. Most combinations attempt to balance the inability of a spatial filter to effectively enhance static regions in an image versus the inability of a temporal filter to effectively enhance dynamic regions in an image. Typical combinations of spatial and temporal filters pass an image through both a spatial and temporal filter. As a result, most combinations of spatial and temporal filters introduce some degree of the disadvantages of both the spatial filter and the temporal filter into the image. The consequence being insufficient resolution of both static and dynamic regions of an image.
Therefore, a need exists for a system and method which may preserve spatial detail in static regions, while avoiding motion lag in dynamic regions. Such a system and method may minimize image noise while also minimizing unwanted artifacts and lag associated with temporal and spatial filters.